from __future__ import annotations


def 				lowerCAmelCase__       (			a__    ,     a__ )      ->Union[str, Any]:




  '''simple docstring'''

  print(f'Vertex\tShortest Distance from vertex {src}' )
  for i, d in enumerate(a__ ):
    print(f'{i}\t\t{d}' )


def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__ )      ->Any:




  '''simple docstring'''

  for j in range(a__ ):
    _UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase           =    (graph[j][k] for k in ["src", "dst", "weight"])
    if distance[u] != float("inf" ) and distance[u] + w < distance[v]:
      return True
  return False


def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__    ,     a__ )      ->list[float]:




  '''simple docstring'''

  _UpperCamelCase           =    [float("inf" )] * vertex_count
  _UpperCamelCase           =    0.0

  for _ in range(vertex_count - 1 ):
    for j in range(a__ ):
      _UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase           =    (graph[j][k] for k in ["src", "dst", "weight"])

      if distance[u] != float("inf" ) and distance[u] + w < distance[v]:
        _UpperCamelCase           =    distance[u] + w

  _UpperCamelCase           =    check_negative_cycle(a__    ,     a__    ,     a__ )
  if negative_cycle_exists:
    raise Exception("Negative cycle found" )

  return distance


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   lowerCamelCase__  =					int(input('''Enter number of vertices: ''').strip())
   lowerCamelCase__  =					int(input('''Enter number of edges: ''').strip())

   lowerCamelCase__  =					[{} for _ in range(E)]

   for i in range(E):
      print('''Edge ''', i + 1)
      lowerCamelCase__,lowerCamelCase__,lowerCamelCase__  =					(
          int(x)
          for x in input('''Enter source, destination, weight: ''').strip().split(''' ''')
      )
      lowerCamelCase__  =					{'''src''': src, '''dst''': dest, '''weight''': weight}

   lowerCamelCase__  =					int(input('''\nEnter shortest path source:''').strip())
   lowerCamelCase__  =					bellman_ford(graph, V, E, source)
   print_distance(shortest_distance, 0)





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCamelCase__  =					{
    '''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Wav2Vec2Config'''],
    '''feature_extraction_wav2vec2''': ['''Wav2Vec2FeatureExtractor'''],
    '''processing_wav2vec2''': ['''Wav2Vec2Processor'''],
    '''tokenization_wav2vec2''': ['''Wav2Vec2CTCTokenizer''', '''Wav2Vec2Tokenizer'''],
}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					[
       '''WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''Wav2Vec2ForAudioFrameClassification''',
       '''Wav2Vec2ForCTC''',
       '''Wav2Vec2ForMaskedLM''',
       '''Wav2Vec2ForPreTraining''',
       '''Wav2Vec2ForSequenceClassification''',
       '''Wav2Vec2ForXVector''',
       '''Wav2Vec2Model''',
       '''Wav2Vec2PreTrainedModel''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					[
       '''TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFWav2Vec2ForCTC''',
       '''TFWav2Vec2Model''',
       '''TFWav2Vec2PreTrainedModel''',
       '''TFWav2Vec2ForSequenceClassification''',
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					[
       '''FlaxWav2Vec2ForCTC''',
       '''FlaxWav2Vec2ForPreTraining''',
       '''FlaxWav2Vec2Model''',
       '''FlaxWav2Vec2PreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
   from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
   from .processing_wavaveca import WavaVecaProcessor
   from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_wavaveca import (
          WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
          WavaVecaForAudioFrameClassification,
          WavaVecaForCTC,
          WavaVecaForMaskedLM,
          WavaVecaForPreTraining,
          WavaVecaForSequenceClassification,
          WavaVecaForXVector,
          WavaVecaModel,
          WavaVecaPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_wavaveca import (
          TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFWavaVecaForCTC,
          TFWavaVecaForSequenceClassification,
          TFWavaVecaModel,
          TFWavaVecaPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_wavaveca import (
          FlaxWavaVecaForCTC,
          FlaxWavaVecaForPreTraining,
          FlaxWavaVecaModel,
          FlaxWavaVecaPreTrainedModel,
      )


else:
   import sys

   lowerCamelCase__  =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




